Dr. Stephen Law
University of Oxford, UK
Biography: Dr Stephen Law is currently Director of the CertHE and Director of Studies, Philosophy at Oxford University Department of Continuing Education. He was previousl Reader and Head of Department at heythrop College, University of London. He holds B.Phil and D.Phil degrees from The University of Oxford, where he held several lectureships and a three year stipendiary Junior Research Fellowship. He has published research in metaphysics, philosophy of language, later Wittgenstein and philosophy of religion. He is perhaps best-known for his popular introductory books on Philosophy, including The Philosophy Gym: 25 Short Adventures in Thinking.
Prof. William Hurst
Wageningen University & Research, Netherlands
Biography: Dr Hurst is an Associate Professor in Data Science and eXtended Realities in the Information Technology Group at Wageningen university. He also founded, and is head, the Social Creative Technologies Lab , which specializes in projects related to game engines, virtual, augmented and mixed reality and Data Science. To-date, he has over 100 international publications and has also been principal investigator for several grants funded by different organizations such as EU/NGI, NWO, InnovateUK, EPSRC. He also has 12 years' teaching experience covering subjects such as Data Science Concepts, eXtended Realities, Data Visualization, Digital Games Content Production, Internet and Web Technologies, and Digital Storytelling.
Title of Speech: Engagement in the Digital Age: Gamification and Immersive Worlds
Abstract: This keynote will examine the transformative potential of creative technologies across diverse application settings, with a particular focus on education, highlighting how these tools can enrich learning experiences and foster deeper engagement. The presentation will explore how advanced sensing technologies, such as physiological and cognitive monitoring tools, can provide meaningful insights into learners’ attention, emotion, and motivation, informing the design of adaptive, learner-centered educational experiences. Additionally, the talk will showcase a range of gamification projects (such as serious games and interactive simulations) that employ game mechanics strategically to enhance participation and sustain motivation over time. Attendees will gain insight into how ‘thoughtfully’ designed gamified experiences can complement traditional pedagogy and support measurable learning gains. Furthermore, the talk will discuss emerging opportunities and challenges associated with metaverse data, as a platform for immersive education, collaborative learning, and experiential exploration.
Prof. Hua Dai
California State University Channel Islands, USA
Biography: Hua Dai is a Professor of Management Information Systems at Martin V. Smith School of Business & Economics of California State University at Channel Islands, USA. Her research interests include electronic commerce, eService, big data and business analytics, blockchain and metaverse, outsourcing, IS cross-cultures, and recently, AI applications in business. Dr. Dai’s research has been published in European Journal of Information Systems, Information & Management, Decision Support Systems, The Data Base for Advances in Information Systems, Electronic Markets, ACM Transactions on Software Engineering and Methodology, Journal of Computer Information Systems, Journal of Electronic Commerce Research, among others.
Title of Speech: Learning With the Copilot: Human–AI Collaboration, Strategy Optimization, And Decision Quality in Programmatic Advertising
Abstract: Artificial intelligence (AI) is transforming programmatic advertising by reshaping how digital media is valued, purchased, and optimized at scale. As algorithmic systems increasingly guide decisions about audience targeting, budget allocation, and performance optimization, the role of human decision makers is evolving from manual operators to strategic collaborators with intelligent systems. Understanding this shift is essential to the future of digital advertising and enterprise decision-making.
This work explores how AI influences decision-making power in programmatic markets and considers the broader implications of transparency, trust, and calibrated reliance in algorithmically mediated environments. Rather than viewing AI solely as a tool for automation or efficiency, programmatic advertising can be understood as a dynamic system in which human judgment and machine intelligence continuously interact and adapt. Effective collaboration depends not only on predictive accuracy but also on system design choices that support learning, strategic thinking, and responsible allocation of media investments.
By situating AI within the economic and institutional foundations of digital advertising, this discussion highlights how transparency and decision support shape not only campaign performance but also brand integrity and the sustainability of the broader media ecosystem. It offers a forward-looking perspective on human–AI collaboration and outlines guiding principles for designing AI systems that strengthen performance, accountability, and long-term value creation in programmatic advertising.
Professor Dr Shuliang Li (李树良)
Fellow (lifelong) of the British Computer Society; Director and Chairman of Loxbridge High-Tech Forum, United Kingdom (https://loxbridge3i.co.uk);
Having held the posts as a Reader & Departmental Research Leader in the UK, and a distinguished & Sichuan 100 Talents professor in China
Loxbridge High-Tech Forum, United Kingdom
Education and Professional Training
Dr Li received his education and professional training from: Haiyang First High School (Shandong, China); Southwest Jiaotong University (China); the University of Edinburgh (UK); the University of Bedfordshire (UK); the University of Oxford (UK); the University of Cambridge (UK); Harvard University (USA); the University of Westminster (UK); Nvidia (USA); Meta (USA); and ARM Cambridge (UK).
Publications
Dr Li has authored and co-authored more than one hundred refereed journal articles, research papers, books, and other scholarly items. His publications have appeared in the international journals including Decision Support Systems, European Journal of Marketing, Expert Systems with Applications, Intelligent Systems in Accounting, Finance and Management, Marketing Intelligence and Planning, Industrial Management & Data Systems, International Journal of Management and Decision Making, Mathematics, and Chinese Journal of Management Science, among others. He has also contributed numerous papers to conference proceedings published by IEEE. His research outputs have been widely downloaded and cited internationally.
Textbook Recognition
Two pages of Dr Li’s research were included in the renowned textbook Decision Support Systems and Intelligent Systems (7th edition, Prentice Hall International, 2005).
Conference leadership
Dr Li has chaired more than thirty international academic and research conferences, with proceedings published by Springer, ACM, and IEEE, and indexed by Ei Compendex, Scopus, and Google Scholar.
Research Excellence
Recognised for sustained international research excellence, Dr Li contributed to the UK Government’s Research Assessment Exercises (RAE) 2001 and 2008, and the Research Excellence Framework (REF) 2014.
Teaching and Supervision
Over the course of his academic career, Dr Li has taught and supervised more than 20,000 undergraduate, postgraduate, and doctoral (PhD) students across the UK, China, and other countries worldwide.
Title of Speech: From technology & models to intelligent civilisation: AI, Computing, and the Map for UHI
Abstract: This keynote speech proposes and structures AI as the transition from the technology & model waves to the future stage, high level and top layer, where artificial intelligence, artificial general intelligence (AGI), in particular, and smart robots will get embedded in daily life and systems: daily life, science, engineering, industries, entertainment, healthcare, transport, government, creative work, education. We link real signals of this great shift or change with the future. Firstly, Elon Musk’s interview discussions about AI abundance and Universal High Income (UHI) highlight how the economy of work could be impacted if robotics and AI accelerate as predicted. Secondly, NVIDIA shows accelerated computing and the compute flywheel driving the era to the next-generation chip roadmap pushing AI into real-world labs and applications. Thirdly, Arm Cambridge partly represents the UK’s strategic position in energy-efficient compute, crucial for scaling AI from cloud to edge while maintaining power and cost to be more sustainable. Fourthly, Google and IBM quantum computers and hybrid quantum–classical–AI will expand and accelerate the speed and frontier of feasible computation for civilization and will also reshape the future economies. Fifthly, large Language Models (LLMs) such as GPT, as popularised through ChatGPT, represent a major step towards widely accessible cognitive tooling for future civilisation, that will support human learning, creativity and problem-solving at scale, assisting with the more connected and informed society and communities. The new era of AI AI and AGI as the foundation of intelligent civilisation Musk, AI & AGI, Economy, work and UHI Nvidia GPUs, compute flywheel and the opportunities The UK and ARM Cambridge perspective Google and IBM quantum computers LLMs and future civilization Risks, trust, ethics and governance.
Dr. Yu Zhao
Tokyo University of Science, Japan
Biography: Dr. Yu Zhao is currently a Junior Associate Professor at the School of Management, Tokyo University of Science. He also serves as a Visiting Lecturer at the Institute of Statistical Mathematics, Japan. He received his Ph.D. in Information Science and Technology from Osaka University. His research focuses on both theoretical and practical aspects of statistical learning theory, operations research, and management science. His analytical approaches include machine learning and algorithmic learning methods, statistical inference and modeling, and mathematical programming. His work has been published in journals such as Omega – The International Journal of Management Science, The European Journal of Operational Research, Expert Systems with Applications, Journal of the Operational Research Society, Journal of Retailing and Consumer Services, and other reputable journals.
Title of Speech: From Uncertainty to Insight: Bridging Efficiency Analysis, Probabilistic Modeling, and Explainable AI
Abstract: Extracting reliable and actionable insight from complex, heterogeneous data remains a central challenge in modern information management. This keynote presents a unified research program that progresses through four stages, namely measure, trust, segment, and understand, where each stage is motivated by the limitations of the preceding one. The first stage employs Data Envelopment Analysis as a nonparametric framework for benchmarking organizational performance by constructing an empirical production frontier, with applications spanning Olympic achievement evaluation, banking assessment, and public utility management. However, when data are noisy, a single efficiency score may not be sufficiently trustworthy for high-stakes decisions. The second stage addresses this limitation by embedding probabilistic structures into frontier estimation. Stochastic Nonparametric Envelopment of Data, forest-based resampling, and hierarchical conditional forests replace fragile point estimates with distributional inference, equipping efficiency scores with confidence intervals and enabling principled productivity change measurement. Yet evaluating all organizations against a single frontier implicitly assumes homogeneity, overlooking structural differences in scale, geography, or operating environment. The third stage tackles this heterogeneity through the Mixed-Copula Mixture Model framework and its open-source implementation pymcmm, which provides a principled approach to clustering datasets containing continuous, discrete, and categorical variables with missing values. By uncovering latent group structures, such as water utility clusters or consumer segments with shared behavioral patterns, the analysis gains both accuracy and policy relevance. The fourth stage leverages explainable AI techniques, specifically SHAP and ICE analyses, to translate complex analytical outputs into transparent, interpretable narratives that support evidence-based action for non-technical stakeholders. Throughout, a running case study in sustainable water utility management demonstrates how these four stages connect end to end. The integration of rigorous measurement, uncertainty quantification, flexible clustering, and post-hoc interpretability offers a cohesive approach to evidence-based decision support in information management.